Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA290347 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The Library of Integrated Cellular Signatures (LINCS) is an NIH program which funds the generation of perturbational profiles across multiple cell and perturbation types, as well as read-outs, at a massive scale. The LINCS Center for Transcriptomics at the Broad Institute uses the L1000 high-throughput gene-expression assay to build a Connectivity Map which seeks to enable the discovery of functional connections between drugs, genes and diseases through analysis of patterns induced by common gene-expression changes. The platform is GPL20573: Broad Institute Human L1000 epsilon http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL20573 For questions or assistance with this dataset, please email the CMap support team at: clue@broadinstitute.org
2015-07-15 | GSE70138 | GEO
Project description:The Library of Integrated Cellular Signatures (LINCS) is an NIH program which funds the generation of perturbational profiles across multiple cell and perturbation types, as well as read-outs, at a massive scale. The LINCS Center for Transcriptomics at the Broad Institute uses the L1000 high-throughput gene-expression assay to build a Connectivity Map which seeks to enable the discovery of functional connections between drugs, genes and diseases through analysis of patterns induced by common gene-expression changes. These files represent L1000 data generated during the LINCS Pilot Phase (2012-2015), as well as profiles generated for more specific purposes, such as assay development and validation projects or testing custom compounds or non-standard cell lines (not part of the core LINCS cell lines). Note: Related GEO projects include (a) Additional L1000 and RNA-Seq data used to validate the assay and improve the inference model, available at GSE92743 (b) The LINCS “production phase” (also termed Phase II, 2015-2020) which is generating an additional cohort of L1000 data, available at GSE70138. The Platform is GPL20573: Broad Institute Human L1000 epsilon https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL20573 For questions or assistance with this dataset, please email the CMap support team at: clue@broadinstitute.org
2017-03-03 | GSE92742 | GEO
Project description:The Library of Integrated Cellular Signatures (LINCS) is an NIH program which funds the generation of perturbational profiles across multiple cell and perturbation types, as well as read-outs, at a massive scale. The LINCS Center for Transcriptomics at the Broad Institute uses the L1000 high-throughput gene-expression assay to profile a range of cellular models and perturbations of cellular state. These data relate to using RNA-Seq datasets from the GTEx consortium (http://www.gtexportal.org/) to validate the L1000 assay and to improve the statistical model used in imputing the transcriptome. The Platform is GPL20573: Broad Institute Human L1000 epsilon https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL20573 For questions or assistance with this dataset, please email the CMap support team at: clue@broadinstitute.org
2017-03-03 | GSE92743 | GEO
Project description:The Library of Integrated Cellular Signatures (LINCS) is an NIH program which funds the generation of perturbational profiles across multiple cell and perturbation types, as well as read-outs, at a massive scale. The LINCS PCCSE uses two high-throughput liquid chromatography-mass spectrometry (LCMS) assays to study the proteomic changes induced by drug and genetic perturbations. P100 monitors ~100 phosphorylated peptides from cellular proteins that serve as a reduced representation of the phosphoproteome. GCP monitors ~60 modified peptides from histones (e.g., methylated, acetylated, and combinations thereof) encompassing nearly every well-studied post-translational modification on the core nucleosomal histone proteins. Closely complementing these assays is the L1000 high-throughput gene-expression assay. The files available here contain P100, GCP, and L1000 data for 90 small-molecule perturbations in six cell lines (five cancer cell lines and a neurodevelopmental cell model). These data were generated under the auspices of the NIH LINCS Program (www.lincsproject.org). Note: Related GEO projects include a large corpus of additional L1000 data, available at GSE92742. The Platform for the L1000 data is GPL20573: Broad Institute Human L1000 epsilon http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL20573 For questions or assistance with this dataset, please email the Connectivity Map support team at: clue@broadinstitute.org
2017-08-24 | GSE101406 | GEO
Project description:Pharmacological and functional genomic screens play an essential role in the discovery and characterization of therapeutic targets and associated pharmacological inhibitors. Although these screens affect thousands of gene products, the typical readout is based on low-complexity rather than genome-wide assays. To address this limitation, we introduce Pooled Library Amplification for Transcriptome Expression (PLATE-Seq), a low-cost, genome-wide mRNA profiling methodology specifically designed to complement high-throughput screening (HTS) assays. Introduction of sample-specific barcodes during reverse transcription supports pooled library construction and low-depth sequencing that is 10 to 20-fold less expensive than conventional RNA-Seq. The use of network-based algorithms to infer protein activity from PLATE-Seq data results in comparable reproducibility to 30M read sequencing. Indeed, PLATE-Seq reproducibility compares favorably to other large-scale perturbational profiling studies such as the Connectivity Map (CMap) and Library of Integrated Network-based Cellular Signatures (LINCS).
2017-04-07 | GSE97460 | GEO
Project description:Drug Toxicity Signature Generation Center (DToxS) at the Icahn School of Medicine at Mount Sinai is an integral part of the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. A key aim of DToxS is to generate both proteomic and transcriptomic signatures that cab predict adverse effects, especially cardiotoxicity, of drugs approved by the Food and Drug Administration. Towards this goal, high throughput shot-gun proteomics experiments (308 cell line/drug combinations + 64 HeLa control lysates + 9 auxiliary treatment samples) have been conducted at the Center for Advanced Proteomics Research at Rutgers-New Jersey Medical School. The integrated proteomic and transcriptomic signatures have been used for computational network analysis to identify cellular signatures of cardiotoxicity that may predict drug-induced toxicity and possible mitigation of such toxicities by mixing different drugs. Both raw and processed proteomics data have been carefully controlled for quality and have been made publicly available via the PRoteomics IDEntifications (PRIDE) database. As such, this broad drug-stimulated proteomic dataset is valuable for the prediction drug toxicities and their mitigation.
2021-10-18 | PXD014791 | Pride
Project description:The Genotype-Tissue Expression (GTEx) project is a collaborative effort that aims to identify correlations between genotype and tissue-specific gene expression levels that will help identify regions of the genome that influence whether and how much a gene is expressed. GTEx is funded through the Common Fund, and managed by the NIH Office of the Director in partnership with the National Human Genome Research Institute, National Institute of Mental Health, the National Cancer Institute, the National Center for Biotechnology Information at the National Library of Medicine, the National Heart, Lung and Blood Institute, the National Institute on Drug Abuse, and the National Institute of Neurological Diseases and Stroke, all part of NIH. This series of 837 samples represents multiple tissues collected from 102 GTEX donors and 1 control cell line. In total, 30 tissue sites are represented including Adipose, Artery, Heart, Lung, Whole Blood, Muscle, Skin, and 11 brain subregions. RNA-seq expression data, robust clinical data, pathological annotations, and genotypes are also available for these samples from dbGaP (http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000424.v2.p1) and the GTEx portal (www.broadinstitute.org/gtex). While GTEx is no longer generating Affymetrix expression data, donor enrollment continues and is expected to reach 1,000 by the end of 2015. Updates to the GTEx data in dbGaP and the GTEx Portal will be made periodically. contributor: GTEx Laboratory, Data Analysis, and Coordinating Center (LDACC) contributor: The Broad Institute of MIT and Harvard (LDACC PIs: Kristin Ardlie and Gaddy Getz)
2013-04-09 | GSE45878 | GEO
Project description:Background: We have previously used the rat 4 day Complete Freund's Adjuvant (CFA) model to screen compounds with potential to reduce osteoarthritic pain. The aim of this study was to identify genes altered in this model of osteoarthritic pain and use this information to infer analgesic potential of compounds based on their own gene expression profiles using the Connectivity Map approach. Results: Using microarrays, we identified differentially expressed genes in L4 and L5 dorsal root ganglia (DRG) from rats that had received intraplantar CFA for 4 days compared to matched, untreated control animals. Analysis of these data indicated that the two groups were distinguishable by differences in genes important in immune responses, nerve growth and regeneration. This list of differentially expressed genes defined a “CFA signature”. We used the Connectivity Map approach to identify pharmacologic agents in the Broad Institute Build02 database that had gene expression signatures that were inversely related (‘negatively connected’) with our CFA signature. To test the predictive nature of the Connectivity Map methodology, we tested phenoxybenzamine (an alpha adrenergic receptor antagonist) – one of the most negatively connected compounds identified in this database - for analgesic activity in the CFA model. Our results indicate that at 10mg/kg, phenoxybenzamine demonstrated analgesia comparable to that of Naproxen in this model. Conclusion: Evaluation of phenoxybenzamine-induced analgesia in the current study lends support to the utility of the Connectivity Map approach for identifying compounds with analgesic properties in the CFA model.
2010-10-07 | GSE24431 | GEO
Project description:The Genotype-Tissue Expression (GTEx) project is a collaborative effort that aims to identify correlations between genotype and tissue-specific gene expression levels that will help identify regions of the genome that influence whether and how much a gene is expressed. GTEx is funded through the Common Fund, and managed by the NIH Office of the Director in partnership with the National Human Genome Research Institute, National Institute of Mental Health, the National Cancer Institute, the National Center for Biotechnology Information at the National Library of Medicine, the National Heart, Lung and Blood Institute, the National Institute on Drug Abuse, and the National Institute of Neurological Diseases and Stroke, all part of NIH. This series of 837 samples represents multiple tissues collected from 102 GTEX donors and 1 control cell line. In total, 30 tissue sites are represented including Adipose, Artery, Heart, Lung, Whole Blood, Muscle, Skin, and 11 brain subregions. RNA-seq expression data, robust clinical data, pathological annotations, and genotypes are also available for these samples from dbGaP (http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000424.v2.p1) and the GTEx portal (www.broadinstitute.org/gtex). While GTEx is no longer generating Affymetrix expression data, donor enrollment continues and is expected to reach 1,000 by the end of 2015. Updates to the GTEx data in dbGaP and the GTEx Portal will be made periodically. contributor: GTEx Laboratory, Data Analysis, and Coordinating Center (LDACC) contributor: The Broad Institute of MIT and Harvard (LDACC PIs: Kristin Ardlie and Gaddy Getz) GTEx samples are collected from deceased donors at low post-mortem intervals and preserved in PAXgene fixative prior to DNA and RNA extraction.
2013-04-09 | E-GEOD-45878 | biostudies-arrayexpress
Project description:We report the perturbational effects of 25 crude venoms on human IMR-32 (neuroblastoma) cells. These data are used to evaluate the VenomSeq transcriptomic data analysis workflow, and to provide the first dataset generated by this workflow. By comparison with data published by the Connectivity Map team, and other free sources of genetic perturbation in the context of disease or drug exposure, we use these data to discover several novel associations between venoms and specific therapeutic actions.
2019-02-15 | GSE126575 | GEO